MeMBer filter for manoeuvring targets

This paper will introduce a new Multitarget Multi-Bernoulli (MeMBer) recursion for tracking targets traveling under multiple motion models. The proposed interacting multiple model MeMBer (IMM-MeMBer) filter uses Jump Markov Models (JMM) to extended the basic MeMBer recursion to allow for multiple motion models. This extension is implemented using both the SMC and GM based MeMBer approximations. The recursive prediction and update equations are presented for both implementations. Each multiple model implementation is validated against its respective standard MeMBer implementation as well as against each other. This validation is done using a simulated scenario containing multiple maneuvering targets. A variety of metrics are observed including target detection capability, estimate accuracy and model likelihood determination.

[1]  Ba-Ngu Vo,et al.  Convergence Analysis of the Gaussian Mixture PHD Filter , 2007, IEEE Transactions on Signal Processing.

[2]  Ratnasingham Tharmarasa,et al.  SMC-PHD-based multi-target tracking with reduced peak extraction , 2009, Optical Engineering + Applications.

[3]  Sumeetpal S. Singh,et al.  Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[4]  D.E. Clark,et al.  An Efficient Track Management Scheme for the Gaussian-Mixture Probability Hypothesis Density Tracker , 2006, 2006 Fourth International Conference on Intelligent Sensing and Information Processing.

[5]  Arnaud Doucet,et al.  Particle filters for state estimation of jump Markov linear systems , 2001, IEEE Trans. Signal Process..

[6]  Ba-Ngu Vo,et al.  Performance evaluation of multi-target tracking using the OSPA metric , 2010, 2010 13th International Conference on Information Fusion.

[7]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[8]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[9]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[10]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[11]  R. Mahler Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[12]  Ba-Ngu Vo,et al.  Closed Form PHD Filtering for Linear Jump Markov Models , 2006, 2006 9th International Conference on Information Fusion.

[13]  K. Punithakumar,et al.  Multiple-model probability hypothesis density filter for tracking maneuvering targets , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Jianjun Yin,et al.  The Gaussian Particle multi-target multi-Bernoulli filter , 2010, 2010 2nd International Conference on Advanced Computer Control.

[15]  Daniel E. Clark,et al.  Convergence results for the particle PHD filter , 2006, IEEE Transactions on Signal Processing.

[16]  Ba-Ngu Vo,et al.  The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations , 2009, IEEE Transactions on Signal Processing.

[17]  Y. Bar-Shalom,et al.  Probability hypothesis density filter for multitarget multisensor tracking , 2005, 2005 7th International Conference on Information Fusion.

[18]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[19]  Ronald Maher,et al.  A survey of PHD filter and CPHD filter implementations , 2007, SPIE Defense + Commercial Sensing.